Land-use changes outside of military installations continue to threaten the military's capacity to sustain its training mission and adapt to future mission requirements. Rapid sprawl development and urbanization, for example, have removed many undeveloped land tracks that provide important habitat for area species, serve critical roles in regional ecosystem processes, and buffer civilian populations from the unwanted byproducts of military training activities (noise, dust, smoke, etc.). As a result of these encroachment pressures, the effective training space of most military installations has substantially diminished, while the potential for nuisance lawsuits and noncompliance with environmental regulations has skyrocketed. Furthermore, Base Realignment and Closure activities and implementation are compounding these issues by increasing on-base populations and expanding the scope of the training mission in the face of continued reductions in effective training spaces.

The objective of this project was to design a computer simulation model, the Regional Simulator (RSim), that integrates land-use changes with ecological effects of changes in noise, water and air quality, and species of special concern and their habitats. RSim was used to project land-use change and its impacts for the five counties in Georgia surrounding and including Fort Benning to illustrate its applicability to other regions and a diversity of resource managers. Data layers that are widely available were used in the model, and four scenarios were evaluated to illustrate RSim's utility and breadth of application.

Technical Approach

RSim was constructed to integrate land-use changes with ecological effects of changes in noise, water and air quality, and the habitats of two species of concern—red-cockaded woodpecker (Picoides borealis) and gopher tortoise (Gopherus polyphemus). A risk assessment approach was used to determine impacts of single and integrated risks. RSim projected changes in land use and resource management and their environmental impacts for the five counties in Georgia surrounding Fort Benning. RSim was designed to integrate environmental effects of on-base training and testing, off-base development of urban areas and roads, and hurricanes. The spatially explicit simulation tool was deployed in a gaming mode so that users can project the ramifications of decisions. The model was run under four scenarios—urban growth, new road-influenced urbanization, a new military training area, or a hurricane.

To access end-user products developed through this research, please visit the Ecosystem-Based Management section on the RC Tools and Training page.


This research effort contributed to workable management and monitoring plans at Fort Benning. The output from RSim provided information that was directly applicable to Fort Benning's Integrated Natural Resources Management Plan. RSim is also designed so that it is broadly applicable to Department of Defense environmental management concerns. The need for applying ecosystem-based management approaches to military lands and regions that contain them is critical because of unique resources on these lands and the fact that conservation issues may jeopardize military missions if not appropriately managed. The RSim model addresses these critical needs by enabling application of ecosystem-based management approaches to military lands and surrounding regions.

 RSim was designed to address key challenges of applying ecosystem-based management in dealing with broad spatial and temporal scales, feedbacks, and cumulative impacts. Environmental management has been constrained by perspectives that address only single criterion, and RSim allows consideration of several criteria at the same time. The regional perspective itself is a major advance with RSim. Historically, environmental concerns have focused on impacts within the installation due to onsite activities. With use of RSim, resource managers are able to examine impacts of the region on the installation, of the installation on the region, and potential feedbacks. Finally, RSim can integrate various stressors and receptors. (Project Completed - 2007)

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